Career Summary

Biography

Stephan K. Chalup is an Associate Professor in Computer Science and Software Engineering at the University of Newcastle, Australia. He received his Ph.D. in Computer Science (Machine Learning) from Queensland University of Technology in Brisbane. He spent his undergraduate years in Germany at the University of Konstanz and later completed a Diplom in Mathematics with Biology (~Masters by Research) at the University of Heidelberg. At the University of Newcastle he runs the Newcastle Robotics Laboratory and the Interdisciplinary Machine Learning Research Group (IMLRG). His research interests include autonomous agents, computer vision, dimensionality reduction, human centered computing, machine learning, medical data analysis, and neural information processing systems.

Research ExpertiseThe Interdisciplinary Machine Learning Research Group (IMLRG) and the Newcastle Robotics Laboratory have the common objective to advance research in the area of Anthropocentric Biocybernetic Computing. It investigates the complex interactions between humans and their environment on all levels including the cell-, circuit-, and body-levels and the ecosystem. When applied to real-world computing and autonomous agents the aim is to develop systems that approximate human-like skills on tasks such as vision processing, facial expression analysis, space representation, and human-robot interaction. Machine learning techniques are employed for fine tuning the parameters of general models until they perform at extraordinary levels of skill on selected tasks. Biologically motivated models are complemented by alternative designs. The strategy is to approximate human-level skills in artificial systems from several different directions, that is, through interdisciplinary projects in collaboration with experts from relevant disciplines (e.g. electrical engineering, architecture, neuroscience, and applied mathematics). Associated projects involve computer vision, data mining, machine learning, pattern recognition, time series analysis, and intelligent system design. Our special interest is on applications of kernel machines and more specifically on methods for non-linear dimensionality reduction or manifold learning.

Journal article (24 outputs)

This article presents a computer vision approach that can detect and classify abstract face-like patterns, including subliminal faces within a scene. This can be regarded as a way... [more]

This article presents a computer vision approach that can detect and classify abstract face-like patterns, including subliminal faces within a scene. This can be regarded as a way of simulating the phenomenon of pareidolia, that is, the tendency of humans to 'see faces' in random structures such as clouds or rocks. The paper describes the system consisting of a component-based face detector and an expression classifier. The face detector creates a number of component images from the original image at different resolutions. A component image is a binary edge image where the edges are segmented into components using a labelling method with a border-following technique. The component images are then overlaid to produce a component height map where large and notable components across all resolutions have high values, while specular and noisy components have low values. The method retains three-shape components, representing two eyes and a mouth, that have height map values that are larger than the noise cut-off value. Support vector machines using scale-invariant feature vectors are applied for ranking these three-shape components by their geometry and size, and their shape semblance to human faces in the training data. The outcome is a facial expression analysis system that uses face components, with the potential to estimate an emotional expression value for a scene by producing an array of emotion scores corresponding to Ekman's seven Universal Facial Expressions of Emotion. An advantage of this technique, when compared to a holistic method, is that the face components are explicitly isolated. This supports a process of abstraction that can facilitate the detection of distorted and minimal face-like patterns.

A new method is proposed for utilising scene information for stereo eye tracking in stereoscopic 3D virtual environments. The approach aims to improve gaze tracking accuracy and r... [more]

A new method is proposed for utilising scene information for stereo eye tracking in stereoscopic 3D virtual environments. The approach aims to improve gaze tracking accuracy and reduce the required user engagement with eye tracking calibration procedures. The approach derives absolute Point of Regard (POR) from the angular velocity of the eyes without user engaged calibration of drift. The method involves reduction of a hypothesis set for the 3D POR via a process of transformation during saccades and intersection with scene geometry during fixations. A basic implementation of this concept has been demonstrated in simulation using the depth buffer of the scene and a particle representation for the hypothesis set. Future research directions will focus on optimisation of the algorithm and improved utilisation of scene information. The technique shows promise in improving gaze tracking techniques in general, including relative paradigms such as electrooculography.

Chang E, Davis J, Chalup S, 'A new look at the enterprise information system life cycle: Introducing the concept of generational change', ICEIS 2003 - Proceedings of the 5th International Conference on Enterprise Information Systems (2003)

This paper discusses the Enterprise Information System (EIS) life cycle and the phases of the EIS development life cycle. It details the stages in the EIS life cycle and the chara... [more]

This paper discusses the Enterprise Information System (EIS) life cycle and the phases of the EIS development life cycle. It details the stages in the EIS life cycle and the characteristics of phases in the system development life cycle and explains where it differs from traditional concepts of software engineering. In particular it defines the concept of generation change and when it is applied to a system. It also describes the nature of the rapid evolution of the EIS and how it results in version or generational change of the system, and how the EIS development life cycle involves a multitude of engineering processes, not just one. This new perspective could lead to the generation of new EIS development methodologies in business modelling, analysis, design, project management and project estimation.